Abstract Accurate and timely observations of wheat phenology and, particularly, of heading date are instrumental for many scientific and technical domains such as wheat ecophysiology, crop breeding, crop management or precision agriculture. Visual annotation of the heading date in situ is a labour-intensive task that may become prohibitive in scientific and technical activities where high-throughput is needed. This study presents an automatic method to estimate wheat heading date from a series of daily images acquired by a fixed RGB camera in the field. A convolutional neural network (CNN) is trained to identify the presence of spikes in small patches. The heading date is then estimated from the dynamics of the spike presence in the patches over time. The method is applied and validated over a large set of 47 experimental sites located in different regions in France, covering three years with nine wheat cultivars. Results show that our method provides good estimates of the heading dates with a root mean square error close to 2 days when compared to the visual scoring from experts. It outperforms the predictions of a phenological model based on the ARCWHEAT crop model calibrated for our local conditions. The potentials and limits of the proposed methodology towards a possible operational implementation in agronomic applications and decision support systems are finally further discussed.

中文翻译：

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一种基于每日原位图像和深度学习的小麦抽穗期自动测年方法

摘要 准确及时地观察小麦物候，尤其是抽穗期，对许多科学和技术领域都有帮助，例如小麦生态生理学、作物育种、作物管理或精准农业。在原位对标题日期进行视觉注释是一项劳动密集型任务，在需要高通量的科学和技术活动中可能会变得令人望而却步。本研究提出了一种自动方法，可从田间固定 RGB 相机获取的一系列日常图像中估计小麦抽穗日期。训练卷积神经网络 (CNN) 以识别小块中尖峰的存在。然后根据斑块中尖峰随时间变化的动态来估计抽穗日期。该方法在位于法国不同地区的大量 47 个试验点上得到应用和验证，涵盖了 3 年的 9 个小麦品种。结果表明，与专家的视觉评分相比，我们的方法提供了对航向日期的良好估计，均方根误差接近 2 天。它优于基于针对我们当地条件校准的 ARCWHEAT 作物模型的物候模型的预测。最后进一步讨论了所提出的方法在农艺应用和决策支持系统中可能的操作实施的潜力和局限性。结果表明，与专家的视觉评分相比，我们的方法提供了对航向日期的良好估计，均方根误差接近 2 天。它优于基于针对我们当地条件校准的 ARCWHEAT 作物模型的物候模型的预测。最后进一步讨论了所提出的方法在农艺应用和决策支持系统中可能的操作实施的潜力和局限性。结果表明，与专家的视觉评分相比，我们的方法提供了对航向日期的良好估计，均方根误差接近 2 天。它优于基于针对我们当地条件校准的 ARCWHEAT 作物模型的物候模型的预测。最后进一步讨论了所提出的方法在农艺应用和决策支持系统中可能的操作实施的潜力和局限性。